Evaluation of global gridded crop models in simulating sugarcane yield in China

中国是全球第三大甘蔗生产国,中国甘蔗产量模拟可服务于全球食糖和乙醇的生产和贸易.全球格点作物模式CLM5-crop和LPJmL已实现对甘蔗的模拟,但它们在中国的模拟能力未知.本文评估结果表明:两个模式均严重低估了甘蔗产量,模拟均不足观测的1/4.CLM5-crop能有技巧地模拟产量的空间分布特征,而LPJmL不能.两个模式均不能合理模拟产量的年际变化,且低估了产量的上升趋势.模式低估甘蔗产量的部分原因是模式假设收割的是甘蔗的穗而非茎.

Climate Change Modelling and Its Roles to Chinese Crops Yield

Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

Introducing a drought index to a crop model can help to reduce the gap between the simulated and statistical yield

A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.

CROPGRO-Soybean Model Calibration and Assessment of Soybean Yield Responses to Climate Change

Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars.

Study on Growth Monitoring and Yield Prediction of Winter Wheat in the South of Shanxi Province Based on MERSI Data and ALMANAC Crop Model

Accurate crop growth monitoring and yield forecasting have important implications for food security and agricultural macro-control. Crop simulation and satellite remote sensing have their own advantages, combining the two can improve the real-time mechanism and accuracy of agricultural monitoring and evaluation. The research is based on the MERSI data carried by China’s new generation Fengyun-3 meteorological satellite, combined with the US ALMANAC crop model, established the NDVI-LAI model and realized the acquisition of LAI data from point to surface. Because of the principle of the relationship between the morphological changes of LAI curve and the growth of crops, an index that can be used to determine the growth of crops is established to realize real-time, dynamic and wide-scale monitoring of winter wheat growth. At the same time, the index was used to select the different key growth stages of winter wheat for yield estimation. The results showed that the relative error of total yield during the filling period was low, nearly 5%. The research results show that the combination of domestic meteorological satellite Fengyun-3 and ALMANAC crop model for crop growth monitoring and yield estimation is feasible, and further expands the application range of domestic satellites.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

The Impact of Climate Change on Crop Yields in Sub-Saharan Africa

This study estimates of the impact of climate change on yields for the four most commonly grown crops (millet, maize, sorghum and cassava) in Sub-Saharan Africa (SSA). A panel data approach is used to relate yields to standard weather variables, such as temperature and precipitation, and sophisticated weather measures, such as evapotranspiration and the standardized precipitation index (SPI). The model is estimated using data for the period 1961-2002 for 37 countries. Crop yields through 2100 are predicted by combining estimates from the panel analysis with climate change predictions from general circulation models (GCMs). Each GCM is simulated under a range of greenhouse gas emissions (GHG) assumptions. Relative to a case without climate change, yield changes in 2100 are near zero for cassava and range from –19% to +6% for maize, from –38% to –13% for millet and from –47% to –7% for sorghum under alternative climate change scenarios.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

OILCROP-SUN Model Relevance for Evaluation of Nitrogen Management of Sunflower Hybrids in Sargodha, Punjab

The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.

OILCROP-SUN Model for Nitrogen Management of Diverse Sunflower (<i>Helianthus annus</i>L.) Hybrids Production under Agro-Climatic Conditions of Sargodha, Pakistan

Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitrogen levels. The effect of nitrogen (N) on growth and yield components of different sunflower (Helianthus annuus L.) hybrids were evaluated under agro-climatic conditions of Sargodha, Pakistan during spring 2013. The experiment was laid out in a randomized complete block design with split plot arrangement having three replications, keeping cultivars in the main plots and nitrogen levels (0, 45, 90,135 and 180 kg/ha) in sub plots. OIL CROP-SUN Model showed that the model was able to simulate the growth and yield of sunflower with an average of 10.44 error% between observed and simulate achene yield (AY). The result of simulation indicates that nitrogen rate of 180 kg/ha produced highest achene yield in S-78 hybrid as compared to other treatments and Hysun-33 cultivar.

基于天气预报的泾惠渠灌区参考作物滕发量预报模型研究

农业节水背景下,对泾惠渠灌区参考作物滕发量预报方法进行研究,选用Hargreaves-Samani公式作为预报模型,运用中国气象数据网泾河站2008年~2020年13个年份的气象数据进行SPSS参数反演,并采用2022年的实测气象数据进行验证,表明适合泾惠渠灌区的Hargreaves-Samani模型参数为C=0.00122、a=14.19、m=0.259。通过验证,83.2%验证值相对误差在20%以内,同时表明Hargreaves-Samani模型进行参考作物滕发量预报时,呈现“夏季>春季>秋季>冬季”的精度分布规律,可为灌区灌溉预报和智慧水利发展提供实用的理论依据。

湖北省小麦潜在产量时空异质性特征及驱动因子分析

气候变化致使气象要素的时空分布格局发生了不同程度的变化,加剧了作物产量空间异质性的形成。因此,评估气候因子对潜在产量的贡献程度,有利于解析区域间潜在产量差异的形成机制,这对于区域内作物的合理规划和缩小区域间产量差具有重要意义。本研究基于1985—2021年湖北省小麦生长期间的气候数据,应用验证后的APSIM-Wheat模型模拟小麦的潜在产量,利用多元回归等方法评估各气候因子对潜在产量变化的贡献程度,解析潜在产量空间异质性的形成机制。结果表明:湖北省小麦潜在产量变化范围为5068.9~7895.3 kg∙hm^(−2),平均潜在产量为7187.6 kg∙hm^(−2)。近35年来,小麦生育期内的平均温度和总降水量分别以0.1℃∙(10a)−1和13.2 mm∙(10a)−1的速率增加,但总太阳辐射以123.3 MJ∙m^(−2)∙(10a)^(−1)的速率下降。这些气候因素变化加剧了生育期内易涝、弱光环境的形成,导致小麦生产潜力降低,潜在产量平均每10年下降422.0 kg∙hm^(−2),降水量的增加对潜在产量下降的贡献最大,相关系数高达−0.73。湖北省小麦潜在产量的空间分布特征为北高南低,南北小麦平均潜在产量和光热熵差距分别为218 kg∙hm^(−2)和0.06 MJ∙m^(−2)∙d^(−1)∙℃^(−1)。光热熵的高度空间异质性是造成潜在产量差异的主要因素,二者相关性高达0.82。受光热熵时空分布不均的影响,荆州小麦拔节至成熟期的平均每日生长速率较襄阳地区低28.5 kg∙hm^(−2)∙d^(−1),最终导致荆州小麦的潜在产量较襄阳地区低。综上,气候变化使得湖北省小麦潜在产量整体呈下降趋势,针对以江汉平原地区为代表的易涝、弱光的小麦生长环境,选育具有高光效和耐涝性的小麦品种对于缩小区域间的产量差距和实现区域粮食总产的全面提高具有重要意义。

基于机器学习和未来气候变化模式的埃塞俄比亚粮食产量预测

对于以农业产业为支柱的埃塞俄比亚,粮食供应和安全对国家安全和人民的生计尤为重要。由于作物生长和气候因素之间的复杂耦合关系,预测气候变化对农作物产量影响具有较大难度,机器学习技术为这种复杂系统变化的预测提供了一种有效途径。本研究利用37个全球气候模式(GCM)的数据以及土壤数据,基于机器学习模型,预测了埃塞俄比亚2021年至2050年5种主要粮食作物在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下的产量变化。经GCM和变量的筛选后,利用梅赫季和贝尔格季中5种主要粮食作物的10个产量数据对直方图梯度提升决策树、极端梯度提升随机森林、轻梯度提升决策树、随机森林、极限树以及K近邻6种机器学习模型进行训练。经过模型评估,选择表现良好的3个模型,采用线性回归算法进行堆叠,然后使用堆叠模型进行预测。研究结果表明,未来30年埃塞俄比亚梅赫季5种主要粮食作物产量变化以增产<2 t·hm^(-2)为主;SSP126情景下的贝尔格季将出现更明显的减产现象,这可能是由于温室效应的减缓降低了CO_(2)的施肥效应。随着人类活动造成的生态环境恶化,研究区农业生产对粮食结构改变和重新分配生产力的需求不断增长,导致农作物生产力向新的适宜地区转移。研究区在SSP126和SSP585情景下将分别因为干旱缓解和温室效应加剧而获得更高的粮食作物生产力。

VPM模型与转产系数结合的吉林省玉米估产

准确估测农作物产量对于保障粮食安全、指导农业生产和调整生产策略等具有重要作用。为解决大范围估产参数确定难、成本高的问题,该研究基于2016–2021年Sentinel-2遥感数据和气象数据等,提出一种综合植被光合作用模型(vegetation photosynthesis model,VPM)与转产系数的产量估测方法,对吉林省30个玉米主产县进行估产研究。结果表明:1)该研究提出的模型估产精度较传统VPM模型表现出更高的准确性和可靠性(决定系数提升0.18;相对均方根误差降低3.24%);2)研究区玉米单产范围为7~13 t/hm^(2),高值区主要集中在中部地区,并且呈现由中部向西北和东南地区递减的趋势;3)模型敏感性分析表明,更精细的转产系数、更高分辨率的玉米空间分布数据和遥感数据能够有效提高模型估产精度。该研究提出的模型可为低成本、大规模、快速精确的估产工作提供解决方案,对实施农业估产具有重要的现实意义和推广价值。

基于磁化微咸水灌溉的作物生长发育特征和产量研究——以青河县为例

为提升高海拔地区微咸水利用效率,以青河县试验区种植的玉米、向日葵和马铃薯为对象,对比分析了磁化微咸水处理对各作物生长发育和产量的影响,进一步研究了磁化微咸水灌溉的向日葵生长模型,分析其生长特征与花盘直径的关系。结果表明:与常规灌溉大田试验相比,研究区3种作物生长发育特征值均显著增大,各作物产量分别提高了28.90%、18.27%和8.19%;磁化微咸水处理的向日葵生长发育各特征指标间相关性很强,建立了向日葵花盘直径关于其特征的多元线性回归模型,拟合系数R 2为0.942;在高海拔地区磁化微咸水灌溉能有效增强作物的生长发育特征,并提高其产量。研究结果可为高海拔灌区推广磁化微咸水设备提供理论依据。

作物生长模型研究现状与展望

作物生长模型由最初的作物生长发育模型发展到农业决策支持模型,在科学研究、农业管理、政策制定等方面发挥着越来越重要的作用。本文首先回顾了作物生长模型的发展过程,并按照模型主要驱动因子,将作物生长模型分为土壤因子、光合作用因子和人为因子驱动3类并分别进行了归纳阐述;然后对典型的模型分别从模型模块、时空尺度、可模拟的作物类型等方面进行列表式对比;并对作物生长模型在气候变化评估、生产管理决策支持、资源管理优化等方面的应用,以及面临的极端条件、复杂农业景观和模型复杂度等挑战进行了总结,在此基础上认为遥感数据同化和孪生农场是其发展方向。

基于同化植被净初级生产力的区域玉米产量估测

为了评价植被净初级生产力NPP作为同化变量提高区域玉米产量估测精度的有效性及其应用于区域玉米产量估测的潜力,选择黑龙江省双鸭山市友谊农场玉米种植区为研究对象,以WOFOST为作物生长动态模型,分别以叶面积指数LAI和植被净初级生产力NPP为同化变量,选用MODIS LAI和NPP产品为遥感观测数据,开展基于遥感观测数据与作物生长模型同化的区域玉米产量估测研究。重点比较了分别以叶面积指数LAI和植被净初级生产力NPP为同化变量的区域玉米产量估测结果精度。结果表明,相较以叶面积指数LAI为同化变量的区域玉米产量估测统计结果(均值为7755 kg/hm^(2),标准差为1303 kg/hm^(2)),以植被净初级生产力NPP为同化变量的区域玉米产量估测统计结果(均值为9214 kg/hm^(2),标准差为190 kg/hm^(2))与研究区域统计结果(均值为8970 kg/hm^(2))更为接近,但在表现玉米产量空间异质性方面稍显不足。以植被净初级生产力NPP为同化变量开展区域作物产量估测是一种可行的数据同化策略,具有较大的应用潜力。

基于Aqua Crop的南宁市甘蔗响应气象干旱情景模拟

南宁市多低山丘陵、岩溶发育,甘蔗种植以雨养为主,气象干旱造成的土壤水分亏缺一直是影响该区域甘蔗生长和产量累积的主要因子,多年来旱灾造成的甘蔗产量损失巨大。为此,本文基于1979—2018年0.1°格点逐日气象数据,计算逐日标准化加权平均降水指数(SWAP)并分析了南宁市气象干旱特征及其在甘蔗生育期的可能发生情景,并采用水分要素驱动的Aqua Crop作物模型,开展了该区域甘蔗生长、生物量及产量累积过程对不同强度、历时的气象干旱的响应机制模拟研究。结果表明:经扩展傅里叶幅度检验法(EFAST)对作物模型参数进行敏感性分析和本地化率定后,该模型模拟研究区的甘蔗产量拟合精度达0.92、均方根误差百分率为3.84%。历时典型气象干旱年情景模拟表明:产量和生物量累积对各强度气象干旱均有显著响应,蒸腾量变化只有在分蘖期和伸长期对气象干旱的响应敏感,而冠层覆盖度对气象干旱的响应具有显著的滞后递减特征。甘蔗各生育期的气象干旱情景模拟发现:萌芽期发生轻、中旱历时达15 d及以上时甘蔗的上述4个要素开始出现显著响应,伸长期发生轻、中和重旱历时为5 d及以上时,甘蔗各要素即出现显著响应,而成熟期基本不受气象干旱影响。各强度气象干旱情景下,甘蔗的产量、生物量、蒸腾量的减少率随干旱历时的变化分别为0~24.0%、0~18.5%及0~15.9%(轻旱历时5~35 d),25.0%~37.0%、20.0%~29.3%及8.0%~24.4%(中旱历时15~45 d),33.5%~40.0%、26.2%~31.7%及18.9%~25.7%(重旱历时35~50 d)。上述成果揭示了研究区甘蔗生长过程及累计产量等与各强度、历时气象干旱之间的定量映射关系,可为南宁市解析气象干旱-土壤水分-甘蔗长势的旱灾链式传递机理、多阶段旱灾预警及旱灾动态风险智慧调控等奠定科学基础。

基于数据同化系统的作物产量预测研究进展

数据同化系统融合了遥感数据和作物生长模型的优势,是实时监测农业生产状况的有力手段。本文在简要介绍作物产量遥感估测方法的基础上,重点对数据同化算法的发展情况、多源遥感数据在数据同化上的应用潜力、数据同化系统的不确定性以及数据同化系统的尺度效应4方面进行论述。并且针对农业应用现状,提出未来应充分挖掘多源遥感数据、多作物生长模型集合和数据算法的优势,最终实现以机理模型为纽带的作物估产模式,并为制定田间管理策略、规划粮食产业布局和制定进出口贸易政策提供有力的数据和技术支撑。

AquaCrop作物模型在黄土塬区夏玉米生产中的适用性评价

为评价Aqua Crop作物模型在黄土塬区的适用性,基于Hsiao等人推荐的玉米参数对模型参数进行调试及验证。在陕西长武地区模拟2003、2004、2005、2007、2008、2010年玉米生育期内生物量、蒸发蒸腾量的变化过程及收获时产量、地上部生物量,将模拟值与收集到的实测值进行对比、分析。结果表明,这6年模拟产量与实测产量间的校正决定系数(Adj)R2为0.9270,相对误差在-2.479至11.182之间;模拟地上部生物量与实测地上部生物量间的Adj.R2为0.7842,模型对产量的模拟效果优于对生物量的模拟;2005年和2008年模拟蒸散量与实测蒸散量间的Adj.R2分别为0.6229和0.7973。模拟效果较好,对黄土塬区夏玉米水分优化管理模拟有重要意义。